SoroushVahidi/scidocs huggingface dataset:
A Deep Learning Approach to Contract Element Extraction
(2017)

We explore how deep learning methods can be used for contract element extraction. We show that a BILSTM operating on word, POS tag, and tokenshape embeddings outperforms the linear sliding-window classifiers of our previous work, without any manually written rules. Further improvements are observed by stacking an additional LSTM on top of the BILSTM, or by adding a CRF layer on top of the BILSTM. The stacked BILSTM-LSTM misclassifies fewer tokens, but the BILSTM-CRF combination performs better when methods are evaluated for their ability to extract entire, possibly multi-token contract elements.
A Miniaturized Dual-Band Frequency Selective Surface (FSS) With Closed Loop and Its Complementary Pattern
(2009)

A single-layer substrate frequency selective surface (FSS) made of miniaturized elements is proposed, with two controllable passbands obtained. Each FSS element consists of a loop wire on the top metal layer and its complementary pattern etched at the bottom one, which provides two transmission poles separated by a transmission zero. An equivalent circuit model is given for predicting the characteristics of the designed FSS, and a good agreement between the simulated and measured transmission coefficients is obtained. Furthermore, the cases of oblique wave incidence and cascading FSSs are also measured and examined.
57.5GHz bandwidth 4.8Vpp swing linear modulator driver for 64GBaud m-PAM systems
(2017)

A novel series-stacked large swing push-pull MOS-HBT driver was implemented in 55nm SiGe BiCMOS technology. The circuit achieves 4.8Vpp differential swing, 57.5GHz band-width and has an output compression point of 12 dBm per side. 4-PAM and 8-PAM eye diagrams were measured at 56 GBaud for a record data rate of 168 Gb/s. 4-PAM 64GBaud eye diagrams were also demonstrated. The circuit consumes 820/600 mW with/without the predriver, for an energy efficiency of 4.88/3.57 pJ/b.
Novel Wideband Transition Between Coplanar Waveguide and Microstrip Line
(2010)

A novel wideband vertical transition for connecting the coplanar waveguide (CPW) to the microstrip line is proposed. This transition can be very useful for millimeter-wave packaging and vertical interconnects. It is multilayered, partly tapered, and consists of only one via interconnect. Two different transitions are designed. The first transition allows connectivity of a CPW with Zc=50 Ω to a microstrip line with Zc=16 Ω with a bandwidth of 10-60 GHz. The second transition has the same characteristic impedance, Zc=50 Ω, at the two ports. In this case, the operating frequency is from 40 MHz to 60 GHz. The return losses of both transitions are generally lower than -10 dB over their indicated frequency ranges, while the maximum measured insertion losses are 1.8 and 2.4 dB for the first and second transition, respectively. To extract the S-parameters of the transitions, a new thru-line technique, based on the standard thru-reflect-line two-tier calibration is introduced. Simulation and experimental results, showing good agreement, are presented and discussed.
Geographic and Genetic Population Differentiation of the Amazonian Chocolate Tree (Theobroma cacao L)
(2008)

Numerous collecting expeditions of Theobroma cacao L. germplasm have been undertaken in Latin-America. However, most of this germplasm has not contributed to cacao improvement because its relationship to cultivated selections was poorly understood. Germplasm labeling errors have impeded breeding and confounded the interpretation of diversity analyses. To improve the understanding of the origin, classification, and population differentiation within the species, 1241 accessions covering a large geographic sampling were genotyped with 106 microsatellite markers. After discarding mislabeled samples, 10 genetic clusters, as opposed to the two genetic groups traditionally recognized within T. cacao, were found by applying Bayesian statistics. This leads us to propose a new classification of the cacao germplasm that will enhance its management. The results also provide new insights into the diversification of Amazon species in general, with the pattern of differentiation of the populations studied supporting the palaeoarches hypothesis of species diversification. The origin of the traditional cacao cultivars is also enlightened in this study.
In-Store Gamification: Testing a Location-Based Treasure Hunt App in a Real Retailing Environment
(2016)

Traditional retailers are facing strong competition from e-commerce. One way to meet this challenge is to follow the marketing movement of focusing on customer experiences. This transformation is based on the notion of engaging customers and one way to drive this engagement is through gamification to support value creation. In this study, we have identified variables affecting intentions to use gamified services and in what ways. For this purpose, we developed an app that generated different levels of gamification by varying the number of game elements. The data from a survey distributed during a field experiment indicates that an increasing level of gamification and technology experience have direct positive associations with intrinsic motivation. Furthermore, intrinsic motivation has a positive direct association with satisfaction, although this is partly mediated by mood. Finally, satisfaction has a positive direct relation with intention to use.
A comparison between Chinese and Caucasian head shapes.
(2010)

Univariate anthropometric data have long documented a difference in head shape proportion between Chinese and Caucasian populations. This difference has made it impossible to create eyewear, helmets and facemasks that fit both groups well. However, it has been unknown to what extend and precisely how the two populations differ from each other in form. In this study, we applied geometric morphometrics to dense surface data to quantify and characterize the shape differences using a large data set from two recent 3D anthropometric surveys, one in North America and Europe, and one in China. The comparison showed the significant variations between head shapes of the two groups and results demonstrated that Chinese heads were rounder than Caucasian counterparts, with a flatter back and forehead. The quantitative measurements and analyses of these shape differences may be applied in many fields, including anthropometrics, product design, cranial surgery and cranial therapy.
Evolution of Communication Technologies for Smart Grid applications
(2013)

The idea of Smart Grid has started to evolve more rapidly with the enhancement in Communication Technologies. Two way communication is a key aspect in realizing Smart Grids and is easily possible with the help of modern day advancements in both wired and wireless communication technologies. This paper discusses some of the major communication technologies which include IEEE specified ZigBee, WiMAX and Wireless LAN (Wi-Fi) technologies, GSM 3G/4G Cellular, DASH 7 and PLC (Power Line Communications), with special focus on their applications in Smart Grids. The Smart Grid environments and domains such as Home Area Automation, Substation Automation, Automated Metering Infrastructure, Vehicle-to-Grid Communications, etc. are considered as priority areas for developing smarter grids. The advancements, challenges and the opportunities present in these priority areas are discussed in this paper. & 2012 Elsevier Ltd. All rights reserved.
Valuating privacy
(2005)

In several experimental auctions, participants put a dollar value on private information before revealing it to a group. An analysis of results show that a trait's desirability in relation to the group played a key role in the amount people demanded to publicize private information. Because people can easily obtain, aggregate, and disperse personal data electronically, privacy is a central concern in the information age. This concern is clear in relation to financial data and genetic information, both of which can lead to identity abuse and discrimination. However, other relatively harmless information can also be abused, including a person's gender, salary, age, marital status, or shopping preferences. What's unclear is whether it's the fear of such abuse that actually causes people's stated hesitance to reveal their data. Our hypothesis - and the motivation for our study - is that people reveal information when they feel that they're somewhat typical or positively atypical compared to the target group. To test this hypothesis, we conducted experiments that elicit the value people place on their private data. We found, with great significance (more than 95 percent statistical confidence) that a linear relationship exists between an individual's belief about a trait and the value he or she places on it. That is, the less desirable the trait, the greater the price a person demands for releasing the information. Furthermore, we found that small deviations in a socially positive direction are associated with a lower asking price.
VMTP: a transport protocol for the next generation of communication systems
(1986)

The Versatile Message Transaction Protocol (VMTP) is a transport-level protocol designed to support remote procedure call, multicast and real-time communication. The protocol is optimized for efficient page-level network file access in particular.
In this paper, we describe the significant aspects of the VMTP design, including the VMTP treatment of sessions, addressing, duplicate suppression, flow control and retransmissions plus its provision for multicast. The VMTP design reflects a change in the use of computer communication as well as a change in the underlying hardware base for the next generation of communication systems. It also challenges certain established notions in the design of protocols.
The synchronous data flow programming language LUSTRE
(1991)

This paper describes the language LUSTRE which is a data flow synchronous language, designed for programming reactive systems-uch as automatic control and monitoring sy s t emsas well as for describing hardware. The data flow aspect of LUSTRE makes it very close to usual description tools in these domains (blockdiagrams, networks of operators, dynamical sample-systems, etc.), and its synchronous interpretation makes it well suited for handling time in programs. Moreover, this synchronous interpretation allows it to be compiled into an efficient sequential program. Finally, the LUSTRE formalism is very similar to temporal logics. This allows the language to be used for both writing programs and expressing program properties, which results in an original program verification methodology.
Self-Similarity Driven Color Demosaicking
(2009)

Demosaicking is the process by which from a matrix of colored pixels measuring only one color component per pixel, red, green, or blue, one can infer a whole color information at each pixel. This inference requires a deep understanding of the interaction between colors, and the involvement of image local geometry. Although quite successful in making such inferences with very small relative error, state-of-the-art demosaicking methods fail when the local geometry cannot be inferred from the neighboring pixels. In such a case, which occurs when thin structures or fine periodic patterns were present in the original, state-of-the-art methods can create disturbing artifacts, known as zipper effect, blur, and color spots. The aim of this paper is to show that these artifacts can be avoided by involving the image self-similarity to infer missing colors. Detailed experiments show that a satisfactory solution can be found, even for the most critical cases. Extensive comparisons with state-of-the-art algorithms will be performed on two different classic image databases.
Input/Output Devices and Interaction Techniques
(2014)

The computing literature often draws a sharp distinction between input and output; computer scientists are used to regarding a screen as a passive output device and a mouse as a pure input device. However, nearly all examples of human-computer interaction require both input and output to do anything useful. For example, what good would a mouse be without the corresponding feedback embodied by the cursor on the screen, as well as the sound and feel of the buttons when they are clicked? The distinction between output devices and input devices becomes even more blurred in the real world. A sheet of paper can be used to both record ideas (input) and display them (output). Clay reacts to the sculptor’s fingers yet also provides feedback through the curvature and texture of its surface. Indeed, the complete and seamless integration of input and output is becoming a common research theme in advanced computer interfaces such as ubiquitous computing (Weiser, 1991) and tangible interaction (Ishii & Ullmer, 1997).
Interactions with big data analytics
(2012)

Increasingly in the 21st century, our daily lives leave behind a detailed digital record: our shifting thoughts and opinions shared on Twitter, our social relationships, our purchasing habits, our information seeking, our photos and videos—even the movements of our bodies and cars. Naturally, for those interested in human behavior, this bounty of personal data is irresistible. Decision makers of all kinds, from company executives to government agencies to researchers and scientists, would like to base their decisions and actions on this data. In response, a new discipline of big data analytics is forming. Fundamentally, big data analytics is a workflow that distills terabytes of low-value data (e.g., every tweet) down to, in some cases, a single bit of high-value data (Should Company X acquire Company Y? Can we reject the null hypothesis?). The goal is to see the big picture from the minutia of our digital lives. It is no surprise today that big data is useful for HCI researchers and user interface design. As one example, A/B testing is a standard practice in the usability community to help determine relative differences in user performance using different interfaces. For many years, we have used strict laboratory conditions to evaluate interfaces, but more recently we have seen the ability to implement those tests quickly and on a large population by running controlled Interactions with Big Data Analytics
ClausIE: clause-based open information extraction
(2013)

We propose ClausIE, a novel, clause-based approach to open information extraction, which extracts relations and their arguments from natural language text. ClausIE fundamentally differs from previous approaches in that it separates the detection of ``useful'' pieces of information expressed in a sentence from their representation in terms of extractions. In more detail, ClausIE exploits linguistic knowledge about the grammar of the English language to first detect clauses in an input sentence and to subsequently identify the type of each clause according to the grammatical function of its constituents. Based on this information, ClausIE is able to generate high-precision extractions; the representation of these extractions can be flexibly customized to the underlying application. ClausIE is based on dependency parsing and a small set of domain-independent lexica, operates sentence by sentence without any post-processing, and requires no training data (whether labeled or unlabeled). Our experimental study on various real-world datasets suggests that ClausIE obtains higher recall and higher precision than existing approaches, both on high-quality text as well as on noisy text as found in the web.
Global Learning of Typed Entailment Rules
(2011)

Extensive knowledge bases of entailment rules between predicates are crucial for applied semantic inference. In this paper we propose an algorithm that utilizes transitivity constraints to learn a globally-optimal set of entailment rules for typed predicates. We model the task as a graph learning problem and suggest methods that scale the algorithm to larger graphs. We apply the algorithm over a large data set of extracted predicate instances, from which a resource of typed entailment rules has been recently released (Schoenmackers et al., 2010). Our results show that using global transitivity information substantially improves performance over this resource and several baselines, and that our scaling methods allow us to increase the scope of global learning of entailment-rule graphs.
TUNABLE AND SWITCHABLE BANDPASS FILTERS USING SLOT-LINE RESONATORS
(2010)

In this paper, novel uniplanar tunable and switchable bandpass filters are designed by using the centrally-loaded slot-line resonator. From the voltage-wave distribution along the resonator, the appropriate location for the loading element is determined to be the center of the slot-line resonator, where the voltages of the fundamental signal and second harmonic are maximum and zero, respectively. As a result, the fundamental frequency can be tuned while the second harmonic remains almost unchanged. For the first time, the properties of the centrally-loaded slot-line resonator are analyzed by using the evenand odd-mode method, and their respective resonant frequencies are derived. The demonstrated tunable bandpass filter can give a 30.9% frequency tuning range with acceptable insertion loss when a varactor is used as the loading element. By replacing the loading varactors with PIN diodes, a switchable bandpass filter is realized in which the attenuation in the fundamental passband can be controlled. In experiments, the switchable bandpass filter exhibits a 2.13 dB insertion loss in the fundamental passband when the PIN diodes are off and more than 49 dB isolation across the passband when the PIN diodes are on. Received 8 October 2010, Accepted 5 November 2010, Scheduled 27 November 2010 Corresponding author: Zhi-Hua Bao (bao.zh@ntu.edu.cn).
ViziQuer: A Tool to Explore and Query SPARQL Endpoints
(2011)

The presented tool uses a novel approach to explore and query a SPARQL endpoint. The tool is simple to use as a user needs only to enter an address of a SPARQL endpoint of one’s interest. The tool will extract and visualize graphically the data schema of the endpoint. The user will be able to overview the data schema and use it to construct a SPARQL query according to the data schema. The tool can be downloaded from http://viziquer.lumii.lv. There is also additional information and help on how to use it in practice.
Knowledge Discovery in Manufacturing Simulations
(2015)

Discrete event simulation studies in a manufacturing context are a powerful instrument when modeling and evaluating processes of various industries. Usually simulation experts conduct simulation experiments for a predetermined system specification by manually varying parameters through educated assumptions and according to a prior defined goal. Moreover, simulation experts try to reduce complexity and number of simulation runs by excluding parameters that they consider as not influential regarding the simulation project scope. On the other hand, today's world of big data technology enables us to handle huge amounts of data. We therefore investigate the potential benefits of designing large scale experiments with a much broader coverage of possible system behavior. In this paper, we propose an approach for applying data mining methods on simulation data in combination with suitable visualization methods in order to uncover relationships in model behavior to discover knowledge that otherwise would have remained hidden. For a prototypical demonstration we used a clustering algorithm to divide large amounts of simulation output datasets into groups of similar performance values and depict those groups through visualizations to conduct a visual investigation process of the simulation data.
Toward Delay-Efficient Game-Aware Data Centers for Cloud Gaming
(2016)

Gaming on demand is an emerging service that has recently started to garner prominence in the gaming industry. Cloud-based video games provide affordable, flexible, and high-performance solutions for end-users with constrained computing resources and enables them to play high-end graphic games on low-end thin clients. Despite its advantages, cloud gaming's Quality of Experience (QoE) suffers from high and varying end-to-end delay. Since the significant part of computational processing, including game rendering and video compression, is performed in data centers, controlling the transfer of information within the cloud has an important impact on the quality of cloud gaming services. In this article, a novel method for minimizing the end-to-end latency within a cloud gaming data center is proposed. We formulate an optimization problem for reducing delay, and propose a Lagrangian Relaxation (LR) time-efficient heuristic algorithm as a practical solution. Simulation results indicate that the heuristic method can provide close-to-optimal solutions. Also, the proposed model reduces end-to-end delay and delay variation by almost 11% and 13.5%, respectively, and outperforms the existing server-centric and network-centric models. As a byproduct, our proposed method also achieves better fairness among multiple competing players by almost 45%, on average, in comparison with existing methods.
The Application of Non-parametric Method and Time Series Analysis to Predict the Machine Repair Times
(2017)

This study focuses on the utilization of non-parametric to assess the distribution of repair times of a machine part as well as the prediction of the future values. There are two folds of objectives, namely, the distribution assessment of the repair times. The diagnostic graph, i.e., histogram and normal probability plot, as well as a non-parametric test, Kolmogorov-Smirnov (KS) method, is utilized to assess the distribution of data. According to the KS test, it can be used effectively to test the distribution of the repair times of a machine which are selected as a case study. Another objective is the prediction of the future repair time required to fix a designated part. The time series analysis based on autoregressive integrated moving average (ARIMA) model is deployed in order to forecast the repair times. It turned out that one of the simplest models of ARIMA, ARIMA (0, 1, 0) or random walk, is the most appropriate model for the prediction and this indicates that the pattern of repair times is non-stationary.
Towards an automated system for intelligent screening of candidates for recruitment using ontology mapping (EXPERT)
(2013)

Many e-recruitment tools for recruiting candidates for jobs have significantly spread in recent years. Companies often receive resumes from candidates using e-recruitment tools or via job portal for each job posting and manually short list qualified applicants. The existing e-recruitment tools have been mainly used for the storage of applicant contact data. In this paper, we present EXPERT, an intelligent tool for screening candidates for recruitment using ontology mapping. EXPERT has three phases in screening candidates for recruitment. In first phase, the system collects candidates’ resumes and constructs ontology document for the features of the candidates. Job openings/job requirements are represented as ontology in the second phase and in third phase, EXPERT maps the job requirement ontology onto the candidate ontology document and retrieves the eligible candidates. Experiment results show that this model improves the accuracy of matching candidates with job requirement.